Solving customer’s problems we go deep into the issue and client’s specific to deliver maximum added value to our clients by created solutions
Developing a customized ML-solutions based on a deep client’s IT-systems, operational processes and data bases analysis
Adapting our own ML-products to Client’s business tasks
Quick but detailed clients IT-systems and business goals and risk analysis and developing recommendations for Machine learning integration
Math problem statement research, state-of-the-art methods research, world leading scientific teams cooperation, developing unique algorithms, proof of concept projects
Extensive practice-oriented course with a useful scientific bases and practical tasks by leaders in applied machine learning data scientists.
Deep Machine learning
Face recognition using video data from specially modernized “smart” camera on board (IoT). High accuracy and precision performance without any “clouds”: no remote server required. Examples of technology facilities and potential tasks: face detection, face recognition, age and gender recognition.
Speaker voice recognition as well as specific incidents identification such as broken glass, gunshots, applause, crying etc. Person identification by voice sample. Examples of technology facilities and potential tasks: automatic summarization, coreference resolution, part-of-speech tagging, named entity recognition, natural language understanding, question answering and so on.
Objects recognition such as people, cars, loaders, specific goods on shelves etc. by video data stream or pictures. Examples of technology facilities and potential tasks: face detection, common object detection, tracking of moving objects, object segmentation.
Integration of the learning neural networks into mobile devices with an opportunity to easily train the system for solving business tasks. For example, different types of specific video and audio analytics on board of smartphone.
Smart predictive analytics based on a wide range of different data provides recommendations or make decisions itself with a flexible reaction to the results. Continuous machine learning with an increase in forecasts precisions. Examples of technology facilities and potential tasks: data exploration, building scoring and predictive models, exploring hidden patterns, missing data reconstruction, outliers filtering, kMeans, Latent Factor Models, Conjoint Analysis, Hybrid Models and so on.
- Video Analytics: ScoreFace
- Audio Analytics: CatchVoice
- Computer Vision
- Embedded Neural Network Technology
- Machine Learning, Deep Learning
Our team consists of young and enthusiastic people devoted to machine learning and AI-innovations
Vladimir has over 15 years’ experience in data science and machine learning. He has both scientific and practical business expertise in data science, machine learning and AI.
Eugeny has over 15 years’ practical experience in marketing, innovation and strategic management in different industries: heavy machinery, electronics, IT.
Expasoft has over 7 years successful experience in the AI market, over 45 projects for 10 industries: medicine and pharma - 6, banking -3, industrial production -5 , oil&gas- 7, IT - 3, security - 4, retail - 9, media -9, telecom - 5, logistics – 2.